Thanks to the perception and reasoning mechanism as well as the well-designed affinity kernels of our ASIS, it obtained the best performance on iShape. In Table REF , ASIS advances GMIS* [1]} by 21.7%, advances other popular approaches by 44.2%. However, there are still some drawbacks to the design of ASIS and some failure cases caused by them. For example in Figure REF , two instances are merged into one. We think that's because the graph merge algorithm is a kind of greedy algorithm, while the greedy algorithm makes optimal decisions locally instead of looking for a global optimum. Hence, ASIS is not robust to false-positive (FP) with high confidence. Also, ASIS fails to connect the two parts of an object if they are far away from each other, for example, the antenna on Figure REF . We think that's because CNN is not good at learning long-range affinity. Another failure case is the branch in Figure REF , ASIS results on iShape-Branch include many parts that have not been merged. Those parts lead to a large number of small FPs, which causes a serious performance drop.
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